Title

EEG-controlled Non-invasive Upper Limb Prosthesis

Lead Author Major

Bioengineering

Lead Author Status

Senior

Second Author Major

Bioengineering

Second Author Status

Senior

Third Author Major

Bioengineering

Third Author Status

Senior

Format

SOECS Senior Project Demonstration

Faculty Mentor Name

Huihui Xu

Faculty Mentor Email

hxu@pacific.edu

Faculty Mentor Department

Bioengineering

Abstract/Artist Statement

Current prosthetic devices do not attempt to bridge the gap between the user’s mind and their prosthesis. For example, electromyography (EMG) technology is widely used in prosthetic designs but primarily rely on contracting remaining muscles to control the device. Solutions that do bridge this gap are invasive in nature. Targeted muscle reinnervation (TMR) is one example that allows patients to control their device when the user thinks of contracting those missing-limb muscles but does require a surgical procedure to implant the electrodes. To solve this problem, our approach is to design a brain-machine interface (BMI) system that connects a user’s mind with a prosthetic device, enabling a user to control the movements of the prosthesis with their thoughts. This is done by connecting a Neurosky Mindwave EEG headset with an Inmoov prosthesis that we built. The headset collects and reads the user’s brainwaves and sends those signals to a MATLAB program, where the data is quantified and processed. The data is then sent to an Arduino program. If the signal reaches a certain threshold, the Arduino program will send out signals to move the servo motors within the prosthetic accordingly, and therefore moving the arm on command. With the success of our project, we expect to have an intelligent system responding to user inputs accurately and reliably. Performance tests such as attempting to grasp a bottle will be conducted and the accuracy of the signal and its response will be recorded and analyzed. Multiple runs will be conducted to ensure significant results. This project is a proof of concept to ensure that non-invasive and economical solutions can be implemented to help people in need of brain-machine interface prostheses.

Location

School of Engineering & Computer Science

Start Date

5-5-2018 3:30 PM

End Date

5-5-2018 4:30 PM

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May 5th, 3:30 PM May 5th, 4:30 PM

EEG-controlled Non-invasive Upper Limb Prosthesis

School of Engineering & Computer Science

Current prosthetic devices do not attempt to bridge the gap between the user’s mind and their prosthesis. For example, electromyography (EMG) technology is widely used in prosthetic designs but primarily rely on contracting remaining muscles to control the device. Solutions that do bridge this gap are invasive in nature. Targeted muscle reinnervation (TMR) is one example that allows patients to control their device when the user thinks of contracting those missing-limb muscles but does require a surgical procedure to implant the electrodes. To solve this problem, our approach is to design a brain-machine interface (BMI) system that connects a user’s mind with a prosthetic device, enabling a user to control the movements of the prosthesis with their thoughts. This is done by connecting a Neurosky Mindwave EEG headset with an Inmoov prosthesis that we built. The headset collects and reads the user’s brainwaves and sends those signals to a MATLAB program, where the data is quantified and processed. The data is then sent to an Arduino program. If the signal reaches a certain threshold, the Arduino program will send out signals to move the servo motors within the prosthetic accordingly, and therefore moving the arm on command. With the success of our project, we expect to have an intelligent system responding to user inputs accurately and reliably. Performance tests such as attempting to grasp a bottle will be conducted and the accuracy of the signal and its response will be recorded and analyzed. Multiple runs will be conducted to ensure significant results. This project is a proof of concept to ensure that non-invasive and economical solutions can be implemented to help people in need of brain-machine interface prostheses.